Infections with highly pathogenic avian H5N1 influenza A viruses (lAV) or Ebola viruses (EBOV) cause severe respiratory disease or hemorrhagic fever with high mortality rates in humans. The limited understanding of how these viruses dysregulate the host response impairs effective treatments for virus induced disease. We hypothesize that comparing host responses to H5N1 lAV, EBOV and a range of virulence mutants will allow delineation of common and virus-specific mechanisms of immune subversion and pathogenicity. Here, we propose a highly integrated systems biology approach to address this hypothesis. We will leverage our existing transcriptome and proteome data from cells and mouse lungs infected with H5N1 lAV and mutant viruses, and in Aim 1, similar proteomics and transcriptomics datasets will be acquired for EBOV-infected cells and mice. In addition, we will perform miRNA profiling, phosphoproteomics, metabolomics and lipidomics analyses for both viruses; quantify immune cell trafficking into infected lung or liver tissues; collect transcriptomics data for immune cell populations isolated from lAV infected lungs; and provide comprehensive data for lAV and EBOV protein interactions with host proteins. Datasets will be acquired with the assistance of several Cores for sample processing, statistical and computational analyses, and integration with data generated for West Nile virus (WNV) under the second Research Project of this contract. The Computational Modeling Core will produce prioritized regulatory target lists for follow-up analysis, and in Aim 2, targets will be experimentally validated using in vitro systems coupled with perturbation of host factor expression or activity. Selected targets then will be validated in vivo by generation of knockout (KO) mice and assessment of the effects of gene KO on the outcome of infection. To allow refinement of computational models and completion of the systems biology paradigm, we will also obtain samples from KO mice for iterative OMICs studies. Collectively, this strategy is expected to facilitate predictive modeling of disease states associated with severe human viral pathogens and identify important regulators of viral pathogenesis that may be targeted for novel intervention strategies.